
import warnings
warnings.filterwarnings("ignore")

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn import preprocessing

import plotly.express as px # https://plotly.com/python/
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

import altair as alt
!pip install altair_saver
from altair_saver import save
import altair_saver


import requests
import sys

#!pip install statsmodels
from statsmodels.tsa.seasonal import seasonal_decompose # Seasonal decomposition method (classic)

# Forecasting Model
#!pip install fbprophet
import fbprophet #print('Prophet %s' % fbprophet.__version__)
from fbprophet import Prophet
from fbprophet.plot import add_changepoints_to_plot # Adding likely spikes that change the trend

!pip install pillow
from PIL import Image

# Analisis supervisado
from sklearn.model_selection import train_test_split

# Metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

# Decision tree method
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.tree import plot_tree

# KNeighborsClassifier, Naive Bayes, Stochastic Gradient Descendent Classifier, Random Forest
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import SGDClassifier # one vs all multiclass
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

# Time Series Clustering
!pip install tslearn
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn.preprocessing import TimeSeriesResampler, \
    TimeSeriesResampler

# Time series KMeans
from tslearn.clustering import TimeSeriesKMeans

#  Preprocessing for numbering categorical series 
from sklearn import preprocessing

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url = 'https://docs.google.com/spreadsheets/d/16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA/export?format=csv&id=16-bnsDdmmgtSxdWbVMboIHo5FRuz76DBxsz_BbsEVWA&gid=0'
df_historico = pd.read_csv(url)
df_historico.head()


df_historico.fillna(0)


df_historico = df_historico[['fecha','osm_admin_level_4','nue_casosconf_diff','nue_fallecidos_diff']]
df_historico.head()


# Deleting Indeterminate and NA (sure indeterminado is not transformed in NA?)
df_historico.drop(df_historico[df_historico.osm_admin_level_4 == 'Indeterminado'].index, inplace=True)
df_historico.drop(df_historico[df_historico.osm_admin_level_4.isna()].index, inplace=True)
df_historico.head()


df_historico.fecha = pd.to_datetime(df_historico.fecha.astype(str), format='%d/%m/%Y')


df_historico.rename(columns={
    'fecha':'index',
    'osm_admin_level_4':'Province/State'
}, inplace=True)


df_historico.head()


df_historico.sort_values(by=['Province/State', 'index'], ascending=True).head()


df_historico.head()


df_deaths = df_historico[['index','Province/State','nue_fallecidos_diff']]
df_confirmed = df_historico[['index','Province/State','nue_casosconf_diff']]


df_deaths.head()


df_confirmed.head(5)


df_confirmed.groupby(['index','Province/State'])['nue_casosconf_diff'].sum().reset_index().head()


df_confirmed.head()


df_confirmed_pivot = pd.pivot_table(df_confirmed, values='nue_casosconf_diff', index=['Province/State'], columns='index', aggfunc=np.sum).fillna(0)
df_confirmed_pivot.head()


df_deaths_pivot = pd.pivot_table(df_deaths, values='nue_fallecidos_diff',
                                 index=['Province/State'], columns='index', aggfunc=np.sum).fillna(0)


df_confirmed_pivot.head()


df_deaths_pivot.head()


df_confirmed_pivot.columns = df_confirmed_pivot.columns.strftime('%-m/%-d/%y')
df_deaths_pivot.columns = df_deaths_pivot.columns.strftime('%-m/%-d/%y')


df_confirmed_pivot.insert(0, 'Country/Region', 'Argentina')
df_deaths_pivot.insert(0, 'Country/Region', 'Argentina')


df_confirmed_pivot.head() # It is not sequentially ordered


df_confirmed_pivot = df_confirmed_pivot.reset_index()
df_deaths_pivot = df_deaths_pivot.reset_index()


df_confirmed_pivot.head()


df_deaths_pivot.head()


for r in range(0,len(df_confirmed_pivot)):
    for c in range(3,len(df_confirmed_pivot.columns)):
        df_confirmed_pivot.iloc[r,c] = df_confirmed_pivot.iloc[r,c - 1] + df_confirmed_pivot.iloc[r,c]


for r in range(0,len(df_deaths_pivot)):
    for c in range(3,len(df_deaths_pivot.columns)):
        df_deaths_pivot.iloc[r,c] = df_deaths_pivot.iloc[r,c - 1] + df_deaths_pivot.iloc[r,c]


df_confirmed_pivot.head()


df_deaths_pivot.head()


# We check the last date of both data frames (in order to sincronize to the world data)
first_day = (df_confirmed_pivot.columns & df_deaths_pivot.columns)[2]
last_day = (df_confirmed_pivot.columns & df_deaths_pivot.columns)[-1] 
print(first_day)
print(last_day)

3/2/20
11/1/20


df_confirmed = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
df_deaths = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')


# If Argentinean data is one day ahead, we delete the last column
arg_last_day = df_confirmed_pivot.columns[df_confirmed_pivot.shape[1] -1 ]
world_last_day = df_confirmed.columns[df_confirmed.shape[1] -1 ]

print(arg_last_day)
print(world_last_day)

if arg_last_day != world_last_day:
  df_confirmed_pivot.drop(df_confirmed_pivot.columns[len(df_confirmed_pivot.columns)-1], axis=1, inplace=True)
  df_deaths_pivot.drop(df_deaths_pivot.columns[len(df_deaths_pivot.columns)-1], axis=1, inplace=True)

11/1/20
10/31/20


df_confirmed.head(10)


df_confirmed.drop(df_confirmed[df_confirmed['Country/Region'] == 'Argentina'].index, inplace=True)
df_deaths.drop(df_deaths[df_deaths['Country/Region'] == 'Argentina'].index, inplace=True)
df_deaths.head()


# Add two columns, at the 2nd and 3rd place, 
df_confirmed_pivot.insert(2, 'Lat', '-38.4161')
df_confirmed_pivot.insert(3, 'Long', '-63.6167')
df_deaths_pivot.insert(2, 'Lat', '-38.4161')
df_deaths_pivot.insert(3, 'Long', '-63.6167')


df_confirmed_pivot.head()


result_confirmed = pd.concat([df_confirmed, df_confirmed_pivot], axis=0, ignore_index=True).fillna(0)
result_deaths = pd.concat([df_deaths, df_deaths_pivot], axis=0, ignore_index=True).fillna(0)


result_confirmed.head()


result_deaths.head()


# Day wise data
result_confirmed = result_confirmed.sort_values(by=['Country/Region'])
result_deaths = result_deaths.sort_values(by=['Country/Region'])


result_confirmed.head()


result_confirmed[result_confirmed['Country/Region'] == 'Argentina']


result_deaths[result_deaths['Country/Region'] == 'Argentina']


result_confirmed
print(result_confirmed.columns[4], result_confirmed.columns[-1])

1/22/20 10/31/20


# Organización de los datos
result_confirmed.head()


# Next, we observe death results start one day later to confirmed cases collection
result_confirmed.columns[5:] , result_deaths.columns[5:], result_deaths.columns

(Index(['1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20', '1/28/20',
        '1/29/20', '1/30/20', '1/31/20', '2/1/20',
        ...
        '10/22/20', '10/23/20', '10/24/20', '10/25/20', '10/26/20', '10/27/20',
        '10/28/20', '10/29/20', '10/30/20', '10/31/20'],
       dtype='object', length=283),
 Index(['1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20', '1/28/20',
        '1/29/20', '1/30/20', '1/31/20', '2/1/20',
        ...
        '10/22/20', '10/23/20', '10/24/20', '10/25/20', '10/26/20', '10/27/20',
        '10/28/20', '10/29/20', '10/30/20', '10/31/20'],
       dtype='object', length=283),
 Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
        '1/24/20', '1/25/20', '1/26/20', '1/27/20',
        ...
        '10/22/20', '10/23/20', '10/24/20', '10/25/20', '10/26/20', '10/27/20',
        '10/28/20', '10/29/20', '10/30/20', '10/31/20'],
       dtype='object', length=288))


#Use columns 1-4 as identifiers, which will be useful for merging later on:
 #id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], 
 
confirmed_df_long = result_confirmed.melt(
    id_vars= result_confirmed.columns[:4], 
    value_vars=result_confirmed.columns[5:], 
    var_name='Date', 
    value_name='Confirmed'
                                        )
 # Death results
deaths_df_long = result_deaths.melt(
    id_vars=result_deaths.columns[:4], 
    value_vars=result_deaths.columns[5:], 
    var_name='Date', 
    value_name='Deaths'
                                        )
display(confirmed_df_long.sample(5))
display(deaths_df_long.sample(5))


# Merging confirmed_df_long and deaths_df_long
full_table = confirmed_df_long.merge(
  right=deaths_df_long, 
  how='left',
  on=['Province/State', 'Country/Region', 'Date', 'Lat', 'Long']                                   )


full_table.head()


# Taking into account the fourth column (Date) and transforming Date as date
full_table[full_table.columns[4]] = pd.to_datetime(full_table[full_table.columns[4]])
full_table.head()


# Finding NA (not expected to find them for previous steps) and non-country objects. 
print(f'Cantidad de Nan values: {full_table.isna().sum()}')

Cantidad de Nan values: Province/State    0
Country/Region    0
Lat               0
Long              0
Date              0
Confirmed         0
Deaths            0
dtype: int64


pd.set_option("display.max_rows", None)
full_table.sample(5)


# Date that are not refered to countries have null latitue and longitud. It is necessary to take them down
ship_rows= full_table.loc[full_table['Long'] == 0]
print(f'Wrong allocated countries: {ship_rows.count()}')

Wrong allocated countries: Province/State    1132
Country/Region    1132
Lat               1132
Long              1132
Date              1132
Confirmed         1132
Deaths            1132
dtype: int64


pd.set_option("display.max_rows", None)
ship_rows.head()
# Observed variables are Diamond Princess, MS Zaandam mixed in columns 0 and 1,
# Province/state and Country/Region


# MS Zaandam has latitude and longitud, so manually detected. 
ship_rows = full_table[full_table.columns[0]].str.contains('Grand Princess') | full_table[full_table.columns[0]].str.contains('Diamond Princess') | full_table[full_table.columns[1]].str.contains('Diamond Princess') | full_table[full_table.columns[1]].str.contains('MS Zaandam')
full_ship = full_table[ship_rows]
full_ship.count()

Province/State    1132
Country/Region    1132
Lat               1132
Long              1132
Date              1132
Confirmed         1132
Deaths            1132
dtype: int64


# Deleting ship rows
full_table = full_table[~(ship_rows)]
full_table.head()


# Rearranging by date and country and aggregating Confirmed (5) and fallen (6)
full_grouped = full_table.groupby([full_table.columns[4], full_table.columns[0],
                                   full_table.columns[1], full_table.columns[2],
                                   full_table.columns[3],])[full_table.columns[5],
                                                           full_table.columns[6]].sum().reset_index()
full_grouped.head(5)


list(full_table['Country/Region']) # it contains US
display(full_table['Country/Region'].str.contains('US').astype(int).sum())
full_grouped['Country/Region'].str.contains('US').astype(int).sum()

283

283


# Confirm 
temp = full_grouped.groupby([full_grouped.columns[1],
                             full_grouped.columns[2],
                             full_grouped.columns[3],
                             full_grouped.columns[4],
                             'Date', ])['Confirmed', 'Deaths'].sum().diff().reset_index()


mask = temp['Country/Region'] != temp['Country/Region'].shift(1)
temp.loc[mask, 'Confirmed'] = np.nan
temp.loc[mask, 'Deaths'] = np.nan

# renaming columns
temp.columns = [full_grouped.columns[1],
                             full_grouped.columns[2],
                full_grouped.columns[3], full_grouped.columns[4],
                            'Date', 'New cases', 'New deaths']

# Merging these additional NA columns to full table (setting NA as 0)

full_grouped = full_grouped.merge(
  right=temp, 
  how='left',
  on=[full_grouped.columns[1],
                             full_grouped.columns[2], 
      full_grouped.columns[3], full_grouped.columns[4], 'Date']
                                    )
full_grouped = full_grouped.fillna(0)
full_grouped.head()

# fixing data types
cols = ['New cases', 'New deaths']
full_grouped[cols] = full_grouped[cols].astype('int')
#full_grouped[cols]


display(full_grouped.dtypes)
full_grouped = full_grouped.astype({"Confirmed": int, "Deaths": int, "Lat": float, "Long": float})
display(full_grouped.dtypes)

Date              datetime64[ns]
Province/State            object
Country/Region            object
Lat                       object
Long                      object
Confirmed                float64
Deaths                   float64
New cases                  int64
New deaths                 int64
dtype: object

Date              datetime64[ns]
Province/State            object
Country/Region            object
Lat                      float64
Long                     float64
Confirmed                  int64
Deaths                     int64
New cases                  int64
New deaths                 int64
dtype: object


# Searching for negative values
full_grouped[cols].min() # The min shows that there are negative values of both cases
neg_num_new_cases = list(filter(lambda x: (x < 0), full_grouped['New cases']))
neg_num_new_deaths = list(filter(lambda x: (x < 0), full_grouped['New deaths']))

len_neg_cases = len(neg_num_new_cases)
len_neg_deaths = len(neg_num_new_deaths)
imp_len_neg_cases = 100*len_neg_cases/len(full_grouped['New cases'])
imp_len_neg_deaths = 100*len_neg_deaths/len(full_grouped['New deaths'])

values = [len_neg_cases, len_neg_deaths]
importance = [imp_len_neg_cases, imp_len_neg_deaths]

#Build temporary df
data_df = {'Values': values, 'Importance(%)': importance}
df_neg =  pd.DataFrame(data=data_df, index=['Cases', 'Deaths'])
df_neg


# We found negative New cases, so we eliminated them

full_grouped['New cases'] = full_grouped['New cases'].apply(lambda x: 0 if x<0 else x)
full_grouped['New deaths'] = full_grouped['New deaths'].apply(lambda x: 0 if x<0 else x)
full_grouped[cols].min() # it checks there are no negative values

New cases     0
New deaths    0
dtype: int64


display(full_grouped.sample(10))


full_grouped['weekdays']=full_grouped.Date.dt.dayofweek+1


full_grouped.head(5)


!git clone https://github.com/zarfer007/Mentoria-2020.git

Cloning into 'Mentoria-2020'...
remote: Enumerating objects: 52, done.
remote: Counting objects: 100% (52/52), done.
remote: Compressing objects: 100% (41/41), done.
remote: Total 52 (delta 16), reused 26 (delta 5), pack-reused 0
Unpacking objects: 100% (52/52), done.


pop = pd.read_csv('/content/Mentoria-2020/population.csv')
pop.rename(columns = {'Country Name': 'Country/Region', '2019': 'Population'}, inplace = True)


pop.head(3)


pop.columns

Index(['Country/Region', 'Country Code', 'Region', 'IncomeGroup',
       'Population'],
      dtype='object')


display(pop['Country/Region'].str.contains('United States').astype(int).sum())
pop = pop.replace({'United States': 'US'}) 
#pop.loc[(pop.'Country/Region']== 'United States')] = 'US'
display(pop['Country/Region'].str.contains('US').astype(int).sum())
pop.head()

1

1


full_grouped = pd.merge(full_grouped, pop, on='Country/Region', how='right')


full_grouped['Country/Region'].str.contains('US').astype(int).sum()

283


full_grouped.head(3)


print(full_grouped.Population.isna().sum())
print(full_grouped.Lat.isna().sum()&full_grouped.Long.isna().sum())

0
98


full_grouped = full_grouped.dropna(subset=['Lat'])
full_grouped.head()


full_grouped.Lat.isna().sum()&full_grouped.Long.isna().sum()

0


# tolist provides the list of the elements of a column
full_grouped["IncomeGroup"].tolist()
# Mapping and replacing could be an option to enumerate it, but since it is categorical recoding it is easier
full_grouped.IncomeGroup = pd.Categorical(full_grouped.IncomeGroup) # Define vble as categorical
full_grouped['IncomeGroup_code'] = full_grouped.IncomeGroup.cat.codes+1 # recode categorical variable
full_grouped.head(10)


#Applying logs to the cumulated series and adding those series to the original one

full_grouped["Confirmed_log"] = np.log(full_grouped.Confirmed)
full_grouped["Deaths_log"] = np.log(full_grouped.Deaths)
full_grouped['Infection_rate'] = full_grouped['Confirmed']/full_grouped['Population']*1000
full_grouped['Mortality_rate'] = full_grouped['Deaths']/full_grouped['Population']*1000
pd.set_option('use_inf_as_na', True)


full_grouped['Infection_rate'] = full_grouped['Infection_rate'].fillna(0)
full_grouped['Mortality_rate'] = full_grouped['Mortality_rate'].fillna(0)


# Full_grouped is the full data set ready for analysis
display(full_grouped.head())
full_grouped.dtypes # log variables are inserted directly as float, which is correctly done

Date                datetime64[ns]
Province/State              object
Country/Region              object
Lat                        float64
Long                       float64
Confirmed                  float64
Deaths                     float64
New cases                  float64
New deaths                 float64
weekdays                   float64
Country Code                object
Region                      object
IncomeGroup               category
Population                 float64
IncomeGroup_code              int8
Confirmed_log              float64
Deaths_log                 float64
Infection_rate             float64
Mortality_rate             float64
dtype: object


# Full grouped index with series starting from the first day cases

full_grouped_ix = full_grouped.groupby(['Date','Country/Region']).sum() # Another dataframe to create subset of full_grouped

#full_grouped_ix = full_grouped

full_grouped_ix= (full_grouped_ix.dropna(subset=['Confirmed_log']).reset_index()) # eliminates confirmed data (in logs)

full_grouped_ix = full_grouped_ix.sort_values(["Country/Region", "Date"], ascending = (True, True)).reset_index()

print(f'The difference in shapes shows {full_grouped.shape[0] - full_grouped_ix.shape[0]} rows were dropped')

The difference in shapes shows 35718 rows were dropped


# Creating the variable trend to compare countries from their starting point (Province/state grouped)

full_grouped_ix['trend'] = full_grouped_ix.groupby('Country/Region').cumcount() + 1

#full_grouped_ix['trend'] = full_grouped_ix.groupby(['Country/Region', 'Province/State']).cumcount() + 1
full_grouped_ix.describe()


#Data urls
url_docker = 'https://cities-api-diplo-datos.herokuapp.com/cities'
url_web = 'https://apis.datos.gob.ar/georef/api/provincias'

swap_names = {'San Miguel de Tucumán': 'Tucumán', 'Resistencia': 'Chaco', 'Posadas': 'Misiones', 
              'San Salvador de Jujuy': 'Jujuy', 'Paraná': 'Entre Ríos'}
avoid_names = ['Mar del Plata', 'Rosario', 'Bahía Blanca', 'La Plata']

#Get data
response_docker = requests.get(url_docker)
data_docker = response_docker.json()

request_web = requests.get(url_web)
data_web = request_web.json()

provinces_docker = data_docker
provinces_web = data_web['provincias']

provinces_names = []
provinces_lat = []
provinces_long = []

# Append data from docker
for province in provinces_docker:
  if province['name'] not in avoid_names:
    provinces_names.append(province['name'])
    provinces_lat.append(province['latitude'])
    provinces_long.append(province['longitude'])
  
# Swap names 
for i in range(len(provinces_names)):
  try:
    provinces_names[i] = swap_names[provinces_names[i]]
  except:
    continue

# Append data from external api
for province in provinces_web:
  if province['nombre'] not in provinces_names:
    provinces_names.append(province['nombre'])
    provinces_lat.append(province['centroide']['lat'])
    provinces_long.append(province['centroide']['lon'])

# Swap names 
for i in range(len(provinces_names)):
  if provinces_names[i] == 'Tierra del Fuego, Antártida e Islas del Atlántico Sur':
    provinces_names[i] = 'Tierra del Fuego'
  if provinces_names[i] == 'Ciudad Autónoma de Buenos Aires':
    provinces_names[i] = 'CABA'

#Build temporary df
data_df = {'Province/State': provinces_names, 'Lat': provinces_lat, 'Long': provinces_long }
df_url =  pd.DataFrame(data=data_df)

# Replace new values to full_grouped
i = 0
for province in provinces_names:
  full_grouped.loc[(full_grouped['Province/State'] ==  province), 'Lat'] = df_url[df_url['Province/State'] == province]['Lat'][i]
  full_grouped.loc[(full_grouped['Province/State'] ==  province), 'Long'] = df_url[df_url['Province/State'] == province]['Long'][i]
  i += 1

full_grouped[full_grouped['Country/Region'] == 'Argentina'][['Province/State', 'Lat', 'Long']].sample(5)


display(full_grouped.columns)
display(full_grouped_ix.columns)

Index(['Date', 'Province/State', 'Country/Region', 'Lat', 'Long', 'Confirmed',
       'Deaths', 'New cases', 'New deaths', 'weekdays', 'Country Code',
       'Region', 'IncomeGroup', 'Population', 'IncomeGroup_code',
       'Confirmed_log', 'Deaths_log', 'Infection_rate', 'Mortality_rate'],
      dtype='object')

Index(['index', 'Date', 'Country/Region', 'Lat', 'Long', 'Confirmed', 'Deaths',
       'New cases', 'New deaths', 'weekdays', 'Population', 'IncomeGroup_code',
       'Confirmed_log', 'Deaths_log', 'Infection_rate', 'Mortality_rate',
       'trend'],
      dtype='object')


stats_cases_world = pd.concat([full_grouped.describe(), full_grouped_ix.describe()], axis=1, sort=False)
stats_cases_world = stats_cases_world.drop(["Lat", "Long", "trend", 'weekdays',
                                            'IncomeGroup_code', "Population",
                                            'index'], axis=1) # 'level_0', 
stats_cases_world = stats_cases_world.drop([ "count","25%", "50%", "75%"], axis=0)
#stats_cases_world.columns = stats_cases_world.columns[:8].union(stats_cases_world.columns[8:]  + '_same_start')
display(np.transpose(stats_cases_world))


full_grouped.describe()


# Comparative data, grouped by date (although not all have cases)
full_grouped_by_date =full_grouped[['Date', 'Country/Region','Confirmed', 'Deaths',
                                    'New cases', 'New deaths', 'Confirmed_log', 'Deaths_log']].groupby('Date').mean()
full_grouped_by_date = full_grouped_by_date.sort_values(by='Date', ascending = True).reset_index() # Date in columns

full_grouped_by_trend =full_grouped_ix[['Date', 'Country/Region','Confirmed', 'Deaths',
                                    'New cases', 'New deaths', 'Confirmed_log', 'Deaths_log', 'trend']].groupby('Date').mean()
full_grouped_by_trend = full_grouped_by_trend.sort_values(by='Date', ascending = True).reset_index() # trend in columns

stats_cases_world_mean = pd.concat([full_grouped_by_date.describe(), full_grouped_by_trend.describe()], axis=1, sort=False)

stats_cases_world_mean = stats_cases_world_mean.drop(["Confirmed_log", "Deaths_log",
                                                      "trend" ], axis=1)
stats_cases_world_mean = stats_cases_world_mean.drop([ "count", "min","25%", "50%", "75%"], axis=0)
print("Numero de casos promedio por día y por país")
display(stats_cases_world_mean)
#print('\n')
print("World cases, from the beginning (first four columns) and the starting day of cases (last four columns")

Numero de casos promedio por día y por país

World cases, from the beginning (first four columns) and the starting day of cases (last four columns


full_grouped.columns

Index(['Date', 'Province/State', 'Country/Region', 'Lat', 'Long', 'Confirmed',
       'Deaths', 'New cases', 'New deaths', 'weekdays', 'Country Code',
       'Region', 'IncomeGroup', 'Population', 'IncomeGroup_code',
       'Confirmed_log', 'Deaths_log', 'Infection_rate', 'Mortality_rate'],
      dtype='object')


# Distribution of dayly averages since January
fig, axes = plt.subplots(3,1)
plt.figure(figsize=(20,10))
#plt.title('Dayly average')


sns.boxplot(ax=axes[0], x=full_grouped_by_date['New cases'])
sns.boxplot(ax=axes[1], x=full_grouped_by_date['New deaths'])
sns.boxplot(ax=axes[2], x=full_grouped_by_date['New deaths'])
sns.boxplot(ax=axes[2], x=full_grouped_by_date['New cases'])
fig.suptitle('Dayly Average')
fig.tight_layout()
fig.subplots_adjust(top=0.88) #3

<Figure size 1440x720 with 0 Axes>


# Distribution of dayly averages since January
plt.figure(figsize=(10,6))
plt.title('Dayly average: New cases')
sns.boxplot(x=full_grouped_by_date['New cases'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83c2ff8160>


plt.figure(figsize=(10,6))
plt.title('Dayly average: New deaths')
sns.boxplot(x=full_grouped_by_date['New deaths'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83c2ff87b8>


plt.figure(figsize=(10,6))
plt.title('Dayly average: New cases')
sns.boxplot(x=full_grouped_by_date['New cases'])
sns.boxplot(x=full_grouped_by_date['New deaths'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83c01891d0>


# Distribution of dayly averages since January
fig, axes = plt.subplots(3,1)
plt.figure(figsize=(20,10))



sns.boxplot(ax=axes[0], x=full_grouped_by_trend['New cases'])
sns.boxplot(ax=axes[1], x=full_grouped_by_trend['New deaths'])
sns.boxplot(ax=axes[2], x=full_grouped_by_trend['New deaths'])
sns.boxplot(ax=axes[2], x=full_grouped_by_trend['New cases'])
fig.suptitle('Dayly Average')
fig.tight_layout()
fig.subplots_adjust(top=0.88)

<Figure size 1440x720 with 0 Axes>


plt.figure(figsize=(10,6))
plt.title('Dayly average: New cases')
sns.boxplot(x=full_grouped_by_trend['New cases'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83c009ada0>


plt.figure(figsize=(10,6))
plt.title('Dayly average: New deaths')
sns.boxplot(x=full_grouped_by_date['New deaths'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83c009af28>


plt.figure(figsize=(10,6))
plt.title('Dayly average: New cases')
sns.boxplot(x=full_grouped_by_trend['New cases'])
sns.boxplot(x=full_grouped_by_trend['New deaths'])

<matplotlib.axes._subplots.AxesSubplot at 0x7f83bff2ba58>


# Confirmed
plt.figure(figsize=(10,6))
full_grouped_by_date['Confirmed'].plot()
plt.title('Daily average aggregate cases')
plt.ylabel('Confirmed')
plt.xlabel('Date')
plt.legend()
plt.show()


# Deaths
plt.figure(figsize=(10,6))
full_grouped_by_date['Deaths'].plot() 
plt.title('Daily average aggregate cases')
plt.ylabel('Deaths')
plt.xlabel('Date')
plt.legend()
plt.show()


# log
fig = plt.figure(figsize=(10,6))
np.log(full_grouped_by_date['Confirmed']).plot()
np.log(full_grouped_by_date['Deaths']).plot()
plt.title('Daily average aggregate cases (in logs)')
plt.ylabel('Confirmed and Deaths')
plt.xlabel('Date')
plt.legend()
plt.show() #2


# Argentinean Data set
full_grouped_Argentina = full_grouped.groupby('Country/Region').get_group('Argentina')


stats_cases_arg = pd.concat([full_grouped.groupby('Country/Region').get_group('Argentina').describe(),
                                   full_grouped_ix.groupby('Country/Region').get_group('Argentina').describe()],
                                   axis=1, sort=False)

stats_cases_arg = stats_cases_arg.drop(["Lat", "Long", "Confirmed_log", "Deaths_log",
                                        "Population", "index", "trend", "weekdays",
                                        "IncomeGroup_code" ], axis=1) # , "level_0"
stats_cases_arg = stats_cases_arg.drop(["count","min", "25%", "50%", "75%"], axis=0)
print("Daily average for a 'typical (average)' province")
display(stats_cases_arg)
print("Argentinean cases, from the beginning (first four columns) and from the day of the first case (last four columns")

Daily average for a 'typical (average)' province

Argentinean cases, from the beginning (first four columns) and from the day of the first case (last four columns


fig = px.scatter_geo(full_grouped, lat=full_grouped.columns[3], lon=full_grouped.columns[4], 
                     color= full_grouped.columns[5], text=full_grouped.columns[2],
                     hover_name=full_grouped.columns[2], size=full_grouped.columns[5],
                     animation_group=full_grouped.columns[5],
                     projection='natural earth',
                     title='World Confirmed Cases')
fig.show()


fig = px.scatter_geo(full_grouped, lat=full_grouped.columns[3], lon=full_grouped.columns[4], 
                     color= full_grouped.columns[6], text=full_grouped.columns[2],
                     hover_name=full_grouped.columns[2], size=full_grouped.columns[6],
                     animation_group=full_grouped.columns[6],
                     projection='natural earth',
                     title='Covid-19 Deaths around the Globe')
fig.show()


fig = px.scatter_geo(full_grouped, lat=full_grouped.columns[3], lon=full_grouped.columns[4], 
                     color= 'Infection_rate', text=full_grouped.columns[2],
                     hover_name=full_grouped.columns[2], size='Infection_rate',
                     animation_group='Infection_rate',
                     projection='natural earth',
                     title='Infection rate per 1000')
fig.show()


fig = px.scatter_geo(full_grouped, lat=full_grouped.columns[3], lon=full_grouped.columns[4], 
                     color= 'Mortality_rate', text=full_grouped.columns[2],
                     hover_name=full_grouped.columns[2], size='Mortality_rate',
                     animation_group='Mortality_rate',
                     projection='natural earth',
                     title='Mortality rate per 1000')
fig.show()


fig = px.scatter_geo(full_grouped_Argentina, lat=full_grouped_Argentina.columns[3], lon=full_grouped_Argentina.columns[4], 
                     color= full_grouped_Argentina.columns[5], text=full_grouped_Argentina.columns[1],
                     hover_name=full_grouped_Argentina.columns[1], size=full_grouped_Argentina.columns[5],
                     animation_group=full_grouped_Argentina.columns[5],
                     scope='south america',
                     center={'lat': -32.14293, 'lon':-63.80175},
                     projection='natural earth',
                     title='Casos confirmados alrededor de Argentina')
fig.show()


fig = px.scatter_geo(full_grouped_Argentina, lat=full_grouped_Argentina.columns[3], lon=full_grouped_Argentina.columns[4], 
                     color= full_grouped_Argentina.columns[6], text=full_grouped_Argentina.columns[1],
                     hover_name=full_grouped_Argentina.columns[1], size=full_grouped_Argentina.columns[6],
                     #animation_frame=full_grouped.columns[6],
                     animation_group=full_grouped_Argentina.columns[6],
                     scope='south america',
                     center={'lat': -32.14293, 'lon':-63.80175},
                     projection='natural earth',
                     title='Decesos en Argentina por Covid_19')
fig.show()


#alt.data_transformers.enable('json')
interval = alt.selection_interval()
alt.data_transformers.disable_max_rows() # not using full data (maxrows)
# New cases
circle = alt.Chart(full_grouped_Argentina.tail(5000)).mark_circle().encode(
    x='monthdate(Date):O',
    y=alt.Y('Country/Region', sort='-x'),
    color=alt.condition(interval,
                        'Country/Region', alt.value('lightgray')),
    size=alt.Size('New cases:Q',
        scale=alt.Scale(range=[0, 1000]),
        legend=alt.Legend(title='Daily new cases')
    ) 
).properties(
    width=800,
    height=300,
    selection=interval
)

#
bars= alt.Chart(full_grouped_Argentina.tail(5000)).mark_bar().encode(
    y=alt.Y('Country/Region', sort='-x'),
    color='Country/Region',
    x='sum(New cases):Q'
).properties(
    width=1000).transform_filter(
    interval)
fig=circle & bars
fig


#alt.data_transformers.enable('json')
interval = alt.selection_interval()
alt.data_transformers.disable_max_rows() # not using full data (maxrows)
# New cases
circle = alt.Chart(full_grouped_Argentina.tail(5000)).mark_circle().encode(
    x='monthdate(Date):O',
    y=alt.Y('Country/Region', sort='-x'),
    color=alt.condition(interval,
                        'Country/Region', alt.value('lightgray')),
    size=alt.Size('New deaths:Q',
        scale=alt.Scale(range=[0, 1000]),
        legend=alt.Legend(title='Daily new cases')
    ) 
).properties(
    width=800,
    height=300,
    selection=interval
)

#
bars= alt.Chart(full_grouped_Argentina.tail(5000)).mark_bar().encode(
    y=alt.Y('Country/Region', sort='-x'),
    color='Country/Region',
    x='sum(New deaths):Q'
).properties(
    width=1000).transform_filter(
    interval)
fig=circle & bars
fig


def newCases(countries = ['Argentina']):
  plt.figure(figsize=(10, 6))
  for country in countries:
    params = (full_grouped['Country/Region'] == country)
    sns.distplot(full_grouped[params]['New cases'], label=country)
  plt.legend()

def newDeaths(countries = ['Argentina']):
  plt.figure(figsize=(10, 6))
  for country in countries:
    params = (full_grouped['Country/Region'] == country)
    sns.distplot(full_grouped[params]['New deaths'], label=country)
  plt.legend()

def cases_and_deaths(countries = ['Argentina', 'Chile']):
  if len(countries) < 2 or len(countries) > 3:
    return print('Entre 2 o 3 paises')
  fig, axes = plt.subplots(len(countries), 2, figsize=(13, 13))
  sns.despine(left=True)
  row = 0
  for country in countries:
    for column in range(2): 
      params = (full_grouped['Country/Region'] == country)
      if column == 1:
        sns.distplot(full_grouped[params]['New deaths'], ax=axes[row, column])
        axes[row, column].title.set_text('Muertes ' + str(country))
      else:
        sns.distplot(full_grouped[params]['New cases'], ax=axes[row, column])
        axes[row, column].title.set_text('Casos ' + str(country))
      axes[row, column].set_ylabel('')
      axes[row, column].set_xlabel('')
      if column == 1:
          row+=1


cases_and_deaths(['Argentina', 'Peru', 'Chile'])


cases_and_deaths(['Argentina', 'Sweden', 'Belgium'])


newCases(['Argentina', 'Sweden'])
newDeaths(['Argentina', 'Sweden'])


newCases(['Chile', 'Peru'])
newDeaths(['Chile', 'Peru'])
newCases(['US', 'Brazil'])
newDeaths(['US', 'Brazil'])


def simpleConfirmedCasesLP(countries = ['Argentina']):
  plt.figure(figsize=(10, 6))
  for country in countries:
    sns.lineplot(full_grouped.Date, full_grouped[full_grouped['Country/Region'] == country]['Confirmed'], label=country)
  plt.title('Casos Confirmados')
  plt.ylabel('Casos')
  plt.xlabel('Fecha')
  plt.legend()
  plt.show()
simpleConfirmedCasesLP()

def simpleConfirmedCasesLog(countries = ['Argentina']):
  plt.figure(figsize=(10, 6))
  for country in countries:
    sns.lineplot(full_grouped.Date, full_grouped[full_grouped['Country/Region'] == country]['Confirmed_log'].fillna(0), label=country)
  plt.title('Casos Confirmados')
  plt.ylabel('Casos')
  plt.xlabel('Fecha')
  plt.legend()
  plt.show()
simpleConfirmedCasesLog()


simpleConfirmedCasesLog(['Belgium', 'Germany', 'Italy','Luxembourg', 'Slovenia','Spain'])
#101


simpleConfirmedCasesLog(['Bolivia', 'Chile', 'Colombia', 'Ecuador', 'Peru'])
#103


simpleConfirmedCasesLP(['US', 'Argentina', 'Brazil', 'Chile','Peru', 'Bolivia', 'Colombia'])
simpleConfirmedCasesLP(['Chile','Peru', 'Bolivia', 'Colombia'])


# returns a line plot of the countries since the start of the dates

def simpleConfirmedCasesLogSameStart(countries = ['Argentina']):
  plt.figure(figsize=(10, 6))
  for country in countries:
    sns.lineplot(full_grouped_ix.trend, full_grouped_ix[full_grouped_ix['Country/Region'] == country]['Confirmed_log'], label=country)

  plt.title('Casos Confirmados')
  plt.ylabel('Casos')
  plt.xlabel('trend')
  plt.legend()
  plt.show()


simpleConfirmedCasesLogSameStart(['Argentina', 'Chile','Peru', 'Bolivia', 'Colombia']) 
simpleConfirmedCasesLogSameStart(['Argentina','US', 'Brazil','Chile','Peru', 'Bolivia', 'Colombia'])
# Todos en fase de máximo?
simpleConfirmedCasesLogSameStart(['Belgium', 'Germany', 'Italy','Luxembourg', 'Slovenia','Spain'])
# Slovenia & Luxembourg well positioned. Slovenia does it much better


simpleConfirmedCasesLogSameStart(['Belgium', 'Germany', 'Italy','Luxembourg', 'Slovenia','Spain'])
#102


simpleConfirmedCasesLogSameStart(['Bolivia', 'Chile', 'Colombia', 'Ecuador', 'Peru'])
#104


params = (full_grouped['Country/Region'] == 'Argentina')
plt.figure(figsize=(10, 6))
sns.lineplot(full_grouped['Date'], full_grouped[params]['Confirmed'], hue=full_grouped[params]['Province/State'])
plt.legend(bbox_to_anchor=(1, 1, 0, 0))
plt.grid()
sns.despine()
plt.xlabel('Fecha')
plt.ylabel('Casos Confirmados')

Text(0, 0.5, 'Casos Confirmados')


params = (full_grouped['Country/Region'] == 'Argentina')
plt.figure(figsize=(10, 6))
sns.lineplot(full_grouped['Date'], full_grouped[params]['Confirmed_log'].fillna(0), hue=full_grouped[params]['Province/State'])
plt.legend(bbox_to_anchor=(1, 1, 0, 0))
plt.grid()
sns.despine()
plt.xlabel('Fecha')
plt.ylabel('Casos Confirmados')
#105

Text(0, 0.5, 'Casos Confirmados')


params = (full_grouped['Country/Region'] == 'Argentina')
plt.figure(figsize=(10, 6))
sns.lineplot(full_grouped['Date'], full_grouped[params]['Deaths_log'].fillna(0), hue=full_grouped[params]['Province/State'])
plt.legend(bbox_to_anchor=(1, 1, 0, 0))
plt.grid()
sns.despine()
plt.xlabel('Fecha')
plt.ylabel('Decesos')

Text(0, 0.5, 'Decesos')


params = (full_grouped['Country/Region'] == 'Argentina')
full_grouped[params]['Confirmed'].describe()

count      6792.000000
mean       9112.974382
std       44619.847312
min           0.000000
25%           2.750000
50%          81.000000
75%        1361.750000
max      549362.000000
Name: Confirmed, dtype: float64


provinces_compare = []

arg_params = (full_grouped['Country/Region'] == 'Argentina')

provinces = full_grouped[arg_params]['Province/State'].unique()

# Small Z score, get provinces near mean
zs_confirmed_arg = np.abs((full_grouped[arg_params]['Confirmed'] - full_grouped[arg_params]['Confirmed'].mean())/full_grouped[arg_params]['Confirmed'].std(ddof=0))
close_mean_confirmed_arg = zs_confirmed_arg<0.0025
mean_provinces = full_grouped[arg_params][close_mean_confirmed_arg]['Province/State'].unique()
for province in mean_provinces:
    provinces_compare.append(province) 

if "Córdoba" not in provinces_compare:
  provinces_compare.append("Córdoba")

print("Provincias a comparar: ")
for province in provinces_compare:
    print(province)

Provincias a comparar: 
Buenos Aires
Jujuy
Tucumán
Chaco
Neuquén
Entre Ríos
Chubut
Tierra del Fuego
Santiago del Estero
Santa Cruz
Córdoba


plt.figure(figsize=(10, 6))
for province in provinces_compare:
  params = (full_grouped['Country/Region'] == 'Argentina') & (full_grouped['Province/State'] == province)
  sns.lineplot(full_grouped['Date'], full_grouped[params]['Confirmed'], label=province)
plt.legend(bbox_to_anchor=(1, 1, 0, 0))
plt.grid()
sns.despine()
plt.xlabel('Fecha')
plt.ylabel('Casos Confirmados')

Text(0, 0.5, 'Casos Confirmados')


plt.figure(figsize=(10, 6))
for province in provinces_compare:
  params = (full_grouped['Country/Region'] == 'Argentina') & (full_grouped['Province/State'] == province)
  sns.lineplot(full_grouped['Date'], full_grouped[params]['Deaths'], label=province)
plt.legend(bbox_to_anchor=(1, 1, 0, 0))
plt.grid()
sns.despine()
plt.xlabel('Fecha')
plt.ylabel('Fallecidos')


litoral =['Formosa', 'Chaco', 'Misiones', 'Corrientes',  'Entre Ríos']
noa=['Jujuy', 'Salta', 'Catamarca', 'La Rioja', 'Tucumán', 'Santiago del Estero']
cuyo=['San Juan', 'Mendoza', 'San Luis']
pampa=['La Pampa', 'Buenos Aires', 'CABA', 'Santa Fe', 'Córdoba']
patagonia=['Neuquén', 'Río Negro', 'Chubut', 'Santa Cruz', 'Tierra del Fuego']

# Argentinean series in logs
def simpleConfirmedCasesLP(provinces = ['Buenos Aires']):
  plt.figure(figsize=(10,6))
  for province in provinces:
    sns.lineplot(full_grouped.Date, full_grouped[full_grouped['Province/State'] == province]['Confirmed_log'].fillna(0), label=province)
 
  plt.title('Confirmed cases (in logs)')
  plt.ylabel('Cases')
  plt.xlabel('Date')
  plt.legend(bbox_to_anchor=(1, 1, 0, 0))
  plt.show()

simpleConfirmedCasesLP()


simpleConfirmedCasesLP(litoral)


simpleConfirmedCasesLP(cuyo)


simpleConfirmedCasesLP(pampa)


simpleConfirmedCasesLP(patagonia)


simpleConfirmedCasesLP(['Buenos Aires', 'CABA','Catamarca'])


simpleConfirmedCasesLP(pampa)
#106


def seasonal_descomposition(country = 'Peru'):
  x=full_grouped_ix.groupby('Country/Region').get_group(country)
  x.set_index('Date', inplace=True) # A necessary step to apply seasonal method
  x=x.loc[~(x['Confirmed_log'] == 0)] # In order to apply multiplicative method (trend related to t-1)
  # Applying the seasonal decomposition method
  x_series = seasonal_decompose(x["Confirmed_log"], model='multiplicative')

  fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1, figsize=(17,16))
  x_series.observed.plot(ax=ax1)
  x_series.trend.plot(ax=ax2)
  x_series.seasonal.plot(ax=ax3)
  x_series.resid.plot(ax=ax4)
seasonal_descomposition()
#107


seasonal_descomposition('Belgium')


def ff_method(country = 'Peru'):
  #Creating the DataFrame
  x_Date=full_grouped_ix.groupby('Country/Region').get_group(country).Date # Array
  x_series=full_grouped_ix.groupby('Country/Region').get_group(country).Confirmed_log
  x_dict = {'Date':x_Date,'Confirmed_log':x_series} # Dictionary
  x = pd.DataFrame(x_dict)

  # Preparing data for Prophet
  df_prophet_aux = x
  df_prophet = pd.DataFrame()
  df_prophet['ds'] = df_prophet_aux['Date']
  df_prophet['y'] = df_prophet_aux['Confirmed_log']
  df_prophet.head(10)

  # Applying the Model Prophet per-se
  m = Prophet(seasonality_mode='multiplicative', changepoint_prior_scale=0.5) # the Prophet class (model)
  m.fit(df_prophet) # fit the model using all data
  future_Confirmed_log=m.make_future_dataframe(periods=7, freq='D')
  forecast_Confirmed_log=m.predict(future_Confirmed_log)

  # Picturing it
  m.plot(forecast_Confirmed_log,xlabel='Time',
                              ylabel='Confirmed_log')

  # Scaling to see if the change in trend is correct (trend flexible)
  fig_air=m.plot(forecast_Confirmed_log, xlabel='Time',
                              ylabel='Confirmed_log')
  a=add_changepoints_to_plot(fig_air.gca(),m,forecast_Confirmed_log)

  # Upper and lower bound predicted values
  #display(forecast_Confirmed_log[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head(10))
  benchmarking = pd.concat([x.Confirmed_log.describe(),
                            forecast_Confirmed_log.yhat.describe(),
                            forecast_Confirmed_log.yhat_lower.describe(),
                            forecast_Confirmed_log.yhat_upper.describe()], axis=1, sort=False)

  print('Comparing observed and forecasted values with lower and upper cuts')
  display(benchmarking)

ff_method()


def full_application(country = 'Peru'):
  # Defining the case country for Death Cases
  y=full_grouped_ix.groupby('Country/Region').get_group(country)
  y.set_index('Date', inplace=True) # A necessary step to apply seasonal method
  # Applying the seasonal decomposition method
  y_series = seasonal_decompose(y["Deaths_log"].dropna(), model='additive') # A goner is a goner

  fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1, figsize=(17,16))
  y_series.observed.plot(ax=ax1)
  y_series.trend.plot(ax=ax2)
  y_series.seasonal.plot(ax=ax3)
  y_series.resid.plot(ax=ax4)



  #Creating the DataFrame
  x_Date=full_grouped_ix.groupby('Country/Region').get_group(country).Date # Array
  x_series=full_grouped_ix.groupby('Country/Region').get_group(country).Confirmed_log
  x_dict = {'Date':x_Date,'Deaths_log':x_series} # Dictionary
  x = pd.DataFrame(x_dict)

  # Preparing data for Prophet
  df_prophet_aux = x
  df_prophet = pd.DataFrame()
  df_prophet['ds'] = df_prophet_aux['Date']
  df_prophet['y'] = df_prophet_aux['Deaths_log']
  df_prophet.head(10)

  # Applying the Model Prophet per-se
  m = Prophet(seasonality_mode='additive', changepoint_prior_scale=0.5) # the Prophet class (model)
  m.fit(df_prophet) # fit the model using all data
  future_Confirmed_log=m.make_future_dataframe(periods=7, freq='D')
  forecast_Confirmed_log=m.predict(future_Confirmed_log)

  # Picturing it
  m.plot(forecast_Confirmed_log,xlabel='Time',
                              ylabel='Deaths_log')

  # Taking into account up or down jumps
  fig_air=m.plot(forecast_Confirmed_log,xlabel='Time',
                              ylabel='Deaths_log')
  a=add_changepoints_to_plot(fig_air.gca(),m,forecast_Confirmed_log)

full_application()


full_application('Brazil')


def seasonal_descomposition_results(country = 'Peru'):
  x=full_grouped_ix.groupby('Country/Region').get_group(country)
  x.set_index('Date', inplace=True) # A necessary step to apply seasonal method
  x=x.loc[~(x['Confirmed_log'] == 0)] # In order to apply multiplicative method (trend related to t-1)
  # Applying the seasonal decomposition method
  x_series = seasonal_decompose(x["Confirmed_log"], model='multiplicative')

  return [x_series.trend, x_series.seasonal, x_series.resid]


len(seasonal_descomposition_results()[0].values) == len(seasonal_descomposition_results()[1].values)


np.full((1, 5), 7)[0]


full_grouped_ts = full_grouped.groupby(['Date', 'Country/Region','Province/State']).sum() # We need to avoid double adding

full_grouped_ts = full_grouped_ts.reset_index()
full_grouped_ts.head(3)


# Relevant data
group_1 = ['Country/Region', 'Lat', 'Long', 'IncomeGroup_code', 'Date', 'Confirmed']
group_2 =['Deaths', 'New cases', 'New deaths', 'weekdays',
          'Population', 'Confirmed_log', 'Deaths_log', 'Infection_rate',
       'Mortality_rate'] # 'Province/State', 'IncomeGroup', 'Region', 'Country Code',
new_cols = group_1+group_2 # Grouping series
set(full_grouped_ts.columns) == set(new_cols) # Setting as columns
full_grouped_cluster = full_grouped_ts[new_cols] # ordering

# drop group 2
full_grouped_cluster.drop(group_2, axis=1, inplace=True)

# Cluster for Monthly data
full_grouped_Monthly = full_grouped_cluster.groupby(['Country/Region',
                                                     'Lat', 'Long',
                                                     'IncomeGroup_code']).resample('M', on='Date').sum()

display(full_grouped_cluster.head(3))
display(full_grouped_Monthly.head(3))


# K means Methods: 1: Euclidean, 2: DBA, 3: Soft-DTW

# Applied on the corresponding Variable
X=full_grouped_Monthly
X.drop(['Lat', 'Long', 'IncomeGroup_code'], axis=1, inplace=True)

n_clusters_=5
method_1 = TimeSeriesKMeans(n_clusters=n_clusters_, verbose=True, random_state=42)
method_2 = TimeSeriesKMeans(n_clusters=n_clusters_, n_init=2, metric="dtw",
                          verbose=True, max_iter_barycenter=10,
                          random_state=42)

# First Method
def Dynamic_clustering(method=method_1,
                       reference="Euclidean k-means",
                       dataframe=X,
                       variable='Group_Km'):
  print(reference)
  
  y_pred = method.fit_predict(X)
  dataframe[variable]=y_pred+1

Dynamic_clustering()

# Second Method
Dynamic_clustering(method = method_2,
                   reference="DBA k-means",
                   dataframe=X,
                   variable='Group_DBA_Km')


full_grouped_Monthly.describe()


full_grouped_Monthly = full_grouped_Monthly.reset_index()
display(full_grouped_Monthly.describe())

def ordinal(method=preprocessing.LabelEncoder(),
            variable=full_grouped_Monthly['Date'],
            variable_1='Date_code'): 
  
  method.fit(variable)

  dict(zip(method.classes_, method.transform(method.classes_)+1))

  #Agregamos una columna con el valor ordinal de la variable considerada
  full_grouped_Monthly[variable_1] = method.transform(variable)+1
ordinal()


# Identifying cluster memberships along the world
fig = px.scatter_geo(full_grouped_Monthly,
                     lat='Lat', lon='Long', 
                     color= "Group_DBA_Km", text='Group_DBA_Km',
                     hover_name='Country/Region', size='Group_DBA_Km',
                     animation_frame="Date_code",
                     animation_group='Date_code',
                     range_color = [1,5],
                     projection='natural earth',
                     title='World Confirmed Cases')
fig.show()

fig = px.scatter_geo(full_grouped_Monthly, lat='Lat', lon='Long', 
                     color= 'Group_DBA_Km', text='Confirmed',
                     hover_name='Country/Region', size='Group_DBA_Km',
                     animation_frame="Date_code",
                     animation_group='Confirmed',
                     range_color = [1,5],
                     projection='natural earth',
                     title='World Confirmed Cases')
fig.show()


# Relevant data
group_1 = ['Country/Region', 'Lat', 'Long', 'IncomeGroup_code', 'Date', 'New cases']
group_2 =['Deaths', 'Confirmed', 'New deaths', 'weekdays',
          'Population', 'Confirmed_log', 'Deaths_log', 'Infection_rate',
       'Mortality_rate'] # 'Province/State', 'IncomeGroup', 'Region', 'Country Code',
new_cols = group_1+group_2 # Grouping series
set(full_grouped_ts.columns) == set(new_cols) # Setting as columns
full_grouped_cluster = full_grouped_ts[new_cols] # ordering

# drop group 2
full_grouped_cluster.drop(group_2, axis=1, inplace=True)

# Cluster for Monthly data
full_grouped_Monthly = full_grouped_cluster.groupby(['Country/Region',
                                                     'Lat', 'Long',
                                                     'IncomeGroup_code']).resample('M', on='Date').sum()

display(full_grouped_cluster.head(3))
display(full_grouped_Monthly.head(3))


# K means Methods: 1: Euclidean, 2: DBA

# Applied on the corresponding Variable
X=full_grouped_Monthly
X.drop(['Lat', 'Long', 'IncomeGroup_code'], axis=1, inplace=True)

n_clusters_=5
method_1 = TimeSeriesKMeans(n_clusters=n_clusters_, verbose=True, random_state=42)
method_2 = TimeSeriesKMeans(n_clusters=n_clusters_, n_init=2, metric="dtw",
                          verbose=True, max_iter_barycenter=10,
                          random_state=42)

# First Method
def Dynamic_clustering(method=method_1,
                       reference="Euclidean k-means",
                       dataframe=X,
                       variable='Group_Km'):
  print(reference)
  
  y_pred = method.fit_predict(X)
  dataframe[variable]=y_pred+1

Dynamic_clustering()

# Second Method
Dynamic_clustering(method = method_2,
                   reference="DBA k-means",
                   dataframe=X,
                   variable='Group_DBA_Km')


full_grouped_Monthly = full_grouped_Monthly.reset_index()


ordinal(variable=full_grouped_Monthly['Date'], variable_1='Date_code')
full_grouped_Monthly.columns


# Identifying cluster memberships along the world
fig = px.scatter_geo(full_grouped_Monthly,
                     lat='Lat', lon='Long', 
                     color= "Group_DBA_Km", text='Group_DBA_Km',
                     hover_name='Country/Region', size='Group_DBA_Km',
                     animation_frame="Date_code",
                     animation_group='Date_code',
                     range_color = [1,5],
                     projection='natural earth',
                     title='World Monthly Cases')
fig.show()

	fecha	dia_inicio	dia_cuarentena_dnu260	osm_admin_level_2	osm_admin_level_4	osm_admin_level_8	tot_casosconf	nue_casosconf_diff	tot_fallecidos	nue_fallecidos_diff	tot_recuperados	tot_terapia	test_RT-PCR_negativos	test_RT-PCR_total	transmision_tipo	informe_tipo	informe_link	observacion	covid19argentina_admin_level_4
0	2/03/2020	1	NaN	Argentina	CABA	NaN	1	1.0	0	0.0	NaN	NaN	NaN	NaN	importado	NaN	https://www.infobae.com/coronavirus/2020/03/03...	no hay informe	capital-federal
1	3/03/2020	2	NaN	Argentina	Indeterminado	NaN	1	0.0	0	0.0	NaN	NaN	NaN	NaN	importado	NaN	NaN	no hay informe	no-data
2	4/03/2020	3	NaN	Argentina	Indeterminado	NaN	1	0.0	0	0.0	NaN	NaN	NaN	NaN	importado	NaN	NaN	no hay informe	no-data
3	5/03/2020	4	NaN	Argentina	Buenos Aires	NaN	2	1.0	2	2.0	NaN	NaN	NaN	NaN	importado	informe nacional	https://www.argentina.gob.ar/sites/default/fil...	NaN	buenos-aires
4	6/03/2020	5	NaN	Argentina	Indeterminado	NaN	2	0.0	2	0.0	NaN	NaN	NaN	NaN	importado	informe nacional	https://www.argentina.gob.ar/sites/default/fil...	NaN	no-data

index	2020-03-02	2020-03-05	2020-03-08	2020-03-09	2020-03-10	2020-03-11	2020-03-12	2020-03-13	2020-03-14	2020-03-15	2020-03-16	2020-03-17	2020-03-18	2020-03-19	2020-03-20	2020-03-21	2020-03-22	2020-03-23	2020-03-24	2020-03-25	2020-03-26	2020-03-27	2020-03-28	2020-03-29	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06	2020-04-07	2020-04-08	2020-04-09	2020-04-10	2020-04-11	2020-04-12	2020-04-13	2020-04-14	...	2020-09-23	2020-09-24	2020-09-25	2020-09-26	2020-09-27	2020-09-28	2020-09-29	2020-09-30	2020-10-01	2020-10-02	2020-10-03	2020-10-04	2020-10-05	2020-10-06	2020-10-07	2020-10-08	2020-10-09	2020-10-10	2020-10-11	2020-10-12	2020-10-13	2020-10-14	2020-10-15	2020-10-16	2020-10-17	2020-10-18	2020-10-19	2020-10-20	2020-10-21	2020-10-22	2020-10-23	2020-10-24	2020-10-25	2020-10-26	2020-10-27	2020-10-28	2020-10-29	2020-10-30	2020-10-31	2020-11-01
Province/State
Buenos Aires	0.0	1.0	8.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	6.0	14.0	9.0	14.0	8.0	5.0	29.0	30.0	27.0	36.0	8.0	16.0	36.0	17.0	10.0	36.0	22.0	26.0	33.0	12.0	34.0	17.0	28.0	19.0	37.0	28.0	29.0	66.0	...	5389.0	6122.0	5600.0	4480.0	2947.0	4544.0	5328.0	5943.0	5407.0	5695.0	4507.0	2648.0	4471.0	5659.0	5222.0	5184.0	5346.0	4074.0	2542.0	2221.0	4666.0	5175.0	5756.0	5199.0	4419.0	2383.0	4206.0	4981.0	5179.0	4854.0	4649.0	3030.0	2170.0	3694.0	4221.0	4238.0	3708.0	3830.0	2354.0	0.0
CABA	1.0	0.0	0.0	1.0	1.0	1.0	10.0	2.0	11.0	6.0	5.0	4.0	10.0	8.0	9.0	29.0	11.0	11.0	29.0	21.0	30.0	27.0	18.0	13.0	34.0	19.0	10.0	24.0	28.0	34.0	32.0	17.0	24.0	18.0	23.0	11.0	46.0	8.0	10.0	22.0	...	929.0	1009.0	926.0	917.0	628.0	787.0	993.0	898.0	924.0	1050.0	801.0	533.0	684.0	883.0	956.0	937.0	874.0	742.0	642.0	499.0	745.0	825.0	830.0	952.0	610.0	517.0	557.0	663.0	740.0	566.0	585.0	464.0	445.0	515.0	598.0	641.0	512.0	504.0	441.0	0.0
Catamarca	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	6.0	2.0	6.0	4.0	67.0	3.0	4.0	8.0	5.0	7.0	6.0	13.0	0.0	9.0	8.0	18.0	7.0	10.0	6.0	14.0	3.0	8.0	6.0	22.0	24.0	27.0	12.0	8.0	25.0	27.0	11.0	33.0	35.0	7.0	28.0	54.0	16.0	83.0	77.0	0.0
Chaco	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	3.0	1.0	1.0	2.0	1.0	5.0	7.0	4.0	9.0	12.0	3.0	3.0	5.0	4.0	12.0	3.0	12.0	3.0	8.0	8.0	0.0	4.0	4.0	15.0	6.0	5.0	0.0	1.0	3.0	31.0	...	86.0	128.0	120.0	160.0	111.0	65.0	143.0	157.0	200.0	200.0	120.0	177.0	101.0	145.0	207.0	156.0	177.0	148.0	199.0	133.0	172.0	192.0	197.0	197.0	167.0	232.0	185.0	193.0	255.0	254.0	231.0	147.0	162.0	137.0	167.0	186.0	130.0	203.0	137.0	0.0
Chubut	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	203.0	162.0	207.0	182.0	100.0	181.0	209.0	156.0	188.0	214.0	149.0	181.0	234.0	322.0	381.0	312.0	205.0	482.0	379.0	348.0	438.0	236.0	245.0	288.0	335.0	150.0	220.0	465.0	531.0	670.0	673.0	351.0	485.0	544.0	318.0	295.0	284.0	309.0	195.0	0.0

index	2020-03-02	2020-03-05	2020-03-08	2020-03-09	2020-03-10	2020-03-11	2020-03-12	2020-03-13	2020-03-14	2020-03-15	2020-03-16	2020-03-17	2020-03-18	2020-03-19	2020-03-20	2020-03-21	2020-03-22	2020-03-23	2020-03-24	2020-03-25	2020-03-26	2020-03-27	2020-03-28	2020-03-29	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06	2020-04-07	2020-04-08	2020-04-09	2020-04-10	2020-04-11	2020-04-12	2020-04-13	2020-04-14	...	2020-09-23	2020-09-24	2020-09-25	2020-09-26	2020-09-27	2020-09-28	2020-09-29	2020-09-30	2020-10-01	2020-10-02	2020-10-03	2020-10-04	2020-10-05	2020-10-06	2020-10-07	2020-10-08	2020-10-09	2020-10-10	2020-10-11	2020-10-12	2020-10-13	2020-10-14	2020-10-15	2020-10-16	2020-10-17	2020-10-18	2020-10-19	2020-10-20	2020-10-21	2020-10-22	2020-10-23	2020-10-24	2020-10-25	2020-10-26	2020-10-27	2020-10-28	2020-10-29	2020-10-30	2020-10-31	2020-11-01
Province/State
Buenos Aires	0.0	1.0	8.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	6.0	14.0	9.0	14.0	8.0	5.0	29.0	30.0	27.0	36.0	8.0	16.0	36.0	17.0	10.0	36.0	22.0	26.0	33.0	12.0	34.0	17.0	28.0	19.0	37.0	28.0	29.0	66.0	...	5389.0	6122.0	5600.0	4480.0	2947.0	4544.0	5328.0	5943.0	5407.0	5695.0	4507.0	2648.0	4471.0	5659.0	5222.0	5184.0	5346.0	4074.0	2542.0	2221.0	4666.0	5175.0	5756.0	5199.0	4419.0	2383.0	4206.0	4981.0	5179.0	4854.0	4649.0	3030.0	2170.0	3694.0	4221.0	4238.0	3708.0	3830.0	2354.0	0.0
CABA	1.0	0.0	0.0	1.0	1.0	1.0	10.0	2.0	11.0	6.0	5.0	4.0	10.0	8.0	9.0	29.0	11.0	11.0	29.0	21.0	30.0	27.0	18.0	13.0	34.0	19.0	10.0	24.0	28.0	34.0	32.0	17.0	24.0	18.0	23.0	11.0	46.0	8.0	10.0	22.0	...	929.0	1009.0	926.0	917.0	628.0	787.0	993.0	898.0	924.0	1050.0	801.0	533.0	684.0	883.0	956.0	937.0	874.0	742.0	642.0	499.0	745.0	825.0	830.0	952.0	610.0	517.0	557.0	663.0	740.0	566.0	585.0	464.0	445.0	515.0	598.0	641.0	512.0	504.0	441.0	0.0
Catamarca	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	6.0	2.0	6.0	4.0	67.0	3.0	4.0	8.0	5.0	7.0	6.0	13.0	0.0	9.0	8.0	18.0	7.0	10.0	6.0	14.0	3.0	8.0	6.0	22.0	24.0	27.0	12.0	8.0	25.0	27.0	11.0	33.0	35.0	7.0	28.0	54.0	16.0	83.0	77.0	0.0
Chaco	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	3.0	1.0	1.0	2.0	1.0	5.0	7.0	4.0	9.0	12.0	3.0	3.0	5.0	4.0	12.0	3.0	12.0	3.0	8.0	8.0	0.0	4.0	4.0	15.0	6.0	5.0	0.0	1.0	3.0	31.0	...	86.0	128.0	120.0	160.0	111.0	65.0	143.0	157.0	200.0	200.0	120.0	177.0	101.0	145.0	207.0	156.0	177.0	148.0	199.0	133.0	172.0	192.0	197.0	197.0	167.0	232.0	185.0	193.0	255.0	254.0	231.0	147.0	162.0	137.0	167.0	186.0	130.0	203.0	137.0	0.0
Chubut	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	203.0	162.0	207.0	182.0	100.0	181.0	209.0	156.0	188.0	214.0	149.0	181.0	234.0	322.0	381.0	312.0	205.0	482.0	379.0	348.0	438.0	236.0	245.0	288.0	335.0	150.0	220.0	465.0	531.0	670.0	673.0	351.0	485.0	544.0	318.0	295.0	284.0	309.0	195.0	0.0

index	Country/Region	3/2/20	3/5/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20	3/23/20	3/24/20	3/25/20	3/26/20	3/27/20	3/28/20	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20	4/8/20	4/9/20	4/10/20	4/11/20	4/12/20	4/13/20	...	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20	11/1/20
Province/State
Buenos Aires	Argentina	0.0	1.0	8.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	6.0	14.0	9.0	14.0	8.0	5.0	29.0	30.0	27.0	36.0	8.0	16.0	36.0	17.0	10.0	36.0	22.0	26.0	33.0	12.0	34.0	17.0	28.0	19.0	37.0	28.0	29.0	...	5389.0	6122.0	5600.0	4480.0	2947.0	4544.0	5328.0	5943.0	5407.0	5695.0	4507.0	2648.0	4471.0	5659.0	5222.0	5184.0	5346.0	4074.0	2542.0	2221.0	4666.0	5175.0	5756.0	5199.0	4419.0	2383.0	4206.0	4981.0	5179.0	4854.0	4649.0	3030.0	2170.0	3694.0	4221.0	4238.0	3708.0	3830.0	2354.0	0.0
CABA	Argentina	1.0	0.0	0.0	1.0	1.0	1.0	10.0	2.0	11.0	6.0	5.0	4.0	10.0	8.0	9.0	29.0	11.0	11.0	29.0	21.0	30.0	27.0	18.0	13.0	34.0	19.0	10.0	24.0	28.0	34.0	32.0	17.0	24.0	18.0	23.0	11.0	46.0	8.0	10.0	...	929.0	1009.0	926.0	917.0	628.0	787.0	993.0	898.0	924.0	1050.0	801.0	533.0	684.0	883.0	956.0	937.0	874.0	742.0	642.0	499.0	745.0	825.0	830.0	952.0	610.0	517.0	557.0	663.0	740.0	566.0	585.0	464.0	445.0	515.0	598.0	641.0	512.0	504.0	441.0	0.0
Catamarca	Argentina	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	6.0	2.0	6.0	4.0	67.0	3.0	4.0	8.0	5.0	7.0	6.0	13.0	0.0	9.0	8.0	18.0	7.0	10.0	6.0	14.0	3.0	8.0	6.0	22.0	24.0	27.0	12.0	8.0	25.0	27.0	11.0	33.0	35.0	7.0	28.0	54.0	16.0	83.0	77.0	0.0
Chaco	Argentina	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	3.0	1.0	1.0	2.0	1.0	5.0	7.0	4.0	9.0	12.0	3.0	3.0	5.0	4.0	12.0	3.0	12.0	3.0	8.0	8.0	0.0	4.0	4.0	15.0	6.0	5.0	0.0	1.0	3.0	...	86.0	128.0	120.0	160.0	111.0	65.0	143.0	157.0	200.0	200.0	120.0	177.0	101.0	145.0	207.0	156.0	177.0	148.0	199.0	133.0	172.0	192.0	197.0	197.0	167.0	232.0	185.0	193.0	255.0	254.0	231.0	147.0	162.0	137.0	167.0	186.0	130.0	203.0	137.0	0.0
Chubut	Argentina	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	203.0	162.0	207.0	182.0	100.0	181.0	209.0	156.0	188.0	214.0	149.0	181.0	234.0	322.0	381.0	312.0	205.0	482.0	379.0	348.0	438.0	236.0	245.0	288.0	335.0	150.0	220.0	465.0	531.0	670.0	673.0	351.0	485.0	544.0	318.0	295.0	284.0	309.0	195.0	0.0

index	Province/State	Country/Region	3/2/20	3/5/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20	3/23/20	3/24/20	3/25/20	3/26/20	3/27/20	3/28/20	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20	4/8/20	4/9/20	4/10/20	4/11/20	4/12/20	...	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20
0	Buenos Aires	Argentina	0.0	1.0	8.0	0.0	1.0	1.0	0.0	1.0	0.0	1.0	1.0	1.0	6.0	14.0	9.0	14.0	8.0	5.0	29.0	30.0	27.0	36.0	8.0	16.0	36.0	17.0	10.0	36.0	22.0	26.0	33.0	12.0	34.0	17.0	28.0	19.0	37.0	28.0	...	5389.0	6122.0	5600.0	4480.0	2947.0	4544.0	5328.0	5943.0	5407.0	5695.0	4507.0	2648.0	4471.0	5659.0	5222.0	5184.0	5346.0	4074.0	2542.0	2221.0	4666.0	5175.0	5756.0	5199.0	4419.0	2383.0	4206.0	4981.0	5179.0	4854.0	4649.0	3030.0	2170.0	3694.0	4221.0	4238.0	3708.0	3830.0	2354.0
1	CABA	Argentina	1.0	0.0	0.0	1.0	1.0	1.0	10.0	2.0	11.0	6.0	5.0	4.0	10.0	8.0	9.0	29.0	11.0	11.0	29.0	21.0	30.0	27.0	18.0	13.0	34.0	19.0	10.0	24.0	28.0	34.0	32.0	17.0	24.0	18.0	23.0	11.0	46.0	8.0	...	929.0	1009.0	926.0	917.0	628.0	787.0	993.0	898.0	924.0	1050.0	801.0	533.0	684.0	883.0	956.0	937.0	874.0	742.0	642.0	499.0	745.0	825.0	830.0	952.0	610.0	517.0	557.0	663.0	740.0	566.0	585.0	464.0	445.0	515.0	598.0	641.0	512.0	504.0	441.0
2	Catamarca	Argentina	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	6.0	2.0	6.0	4.0	67.0	3.0	4.0	8.0	5.0	7.0	6.0	13.0	0.0	9.0	8.0	18.0	7.0	10.0	6.0	14.0	3.0	8.0	6.0	22.0	24.0	27.0	12.0	8.0	25.0	27.0	11.0	33.0	35.0	7.0	28.0	54.0	16.0	83.0	77.0
3	Chaco	Argentina	0.0	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	4.0	3.0	1.0	1.0	2.0	1.0	5.0	7.0	4.0	9.0	12.0	3.0	3.0	5.0	4.0	12.0	3.0	12.0	3.0	8.0	8.0	0.0	4.0	4.0	15.0	6.0	5.0	0.0	1.0	...	86.0	128.0	120.0	160.0	111.0	65.0	143.0	157.0	200.0	200.0	120.0	177.0	101.0	145.0	207.0	156.0	177.0	148.0	199.0	133.0	172.0	192.0	197.0	197.0	167.0	232.0	185.0	193.0	255.0	254.0	231.0	147.0	162.0	137.0	167.0	186.0	130.0	203.0	137.0
4	Chubut	Argentina	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	203.0	162.0	207.0	182.0	100.0	181.0	209.0	156.0	188.0	214.0	149.0	181.0	234.0	322.0	381.0	312.0	205.0	482.0	379.0	348.0	438.0	236.0	245.0	288.0	335.0	150.0	220.0	465.0	531.0	670.0	673.0	351.0	485.0	544.0	318.0	295.0	284.0	309.0	195.0

Quo Vadis Covid-19?¶

Actividades¶

Análisis y Curación de Datos¶

Adding columns for 'New cases' and 'New deaths¶

Fixing proper type of objects to observed cases¶

Search and drop of negative values (if not significant)¶

Adding Weekdays¶

Adding population¶

US is as United States, renaming¶

Adding a IncomeGroup column as ordinal¶

Transforming series in logs and adding Infection and Mortality rates¶

Creating a subset without NaN¶

Adding a trend series (starting from the first day case)¶

Analysis y Visualización de datos¶

Average Statistics (see following cells)¶

Distribución de la media, diaria y diaria con casos¶

ARGENTINA¶

Visualización view¶

The World¶

ARGENTINA¶

Distribution Plots¶

LINEAR & LOG EVOLUTION¶

ARGENTINA¶

Seasonal decomposition and Facebook Forecasting Model¶

Seasonal decomposition¶

Case Model Peru¶

Applying Facebook Forecasting Method¶

Death cases, full application¶

Brazil Case¶

Time Series Clustering¶

Organizing the data¶

Applying clustering methods¶

Organizing data for visualization¶

Applying to New Monthly cases¶

index	2020-03-02	2020-03-05	2020-03-08	2020-03-09	2020-03-10	2020-03-11	2020-03-12	2020-03-13	2020-03-14	2020-03-15	2020-03-16	2020-03-17	2020-03-18	2020-03-19	2020-03-20	2020-03-21	2020-03-22	2020-03-23	2020-03-24	2020-03-25	2020-03-26	2020-03-27	2020-03-28	2020-03-29	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06	2020-04-07	2020-04-08	2020-04-09	2020-04-10	2020-04-11	2020-04-12	2020-04-13	2020-04-14	...	2020-09-23	2020-09-24	2020-09-25	2020-09-26	2020-09-27	2020-09-28	2020-09-29	2020-09-30	2020-10-01	2020-10-02	2020-10-03	2020-10-04	2020-10-05	2020-10-06	2020-10-07	2020-10-08	2020-10-09	2020-10-10	2020-10-11	2020-10-12	2020-10-13	2020-10-14	2020-10-15	2020-10-16	2020-10-17	2020-10-18	2020-10-19	2020-10-20	2020-10-21	2020-10-22	2020-10-23	2020-10-24	2020-10-25	2020-10-26	2020-10-27	2020-10-28	2020-10-29	2020-10-30	2020-10-31	2020-11-01
Province/State
Buenos Aires	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	2.0	1.0	1.0	0.0	2.0	0.0	2.0	2.0	3.0	1.0	1.0	2.0	4.0	1.0	6.0	1.0	4.0	4.0	1.0	2.0	...	291.0	278.0	240.0	173.0	67.0	193.0	248.0	176.0	3199.0	149.0	92.0	58.0	207.0	148.0	186.0	187.0	239.0	187.0	147.0	131.0	170.0	164.0	193.0	137.0	191.0	15.0	197.0	144.0	106.0	192.0	139.0	96.0	86.0	144.0	177.0	122.0	138.0	116.0	113.0	0.0
CABA	0.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	3.0	2.0	2.0	6.0	1.0	3.0	1.0	1.0	2.0	...	34.0	19.0	46.0	59.0	62.0	38.0	52.0	118.0	1.0	44.0	20.0	57.0	57.0	51.0	63.0	67.0	38.0	73.0	20.0	55.0	49.0	51.0	57.0	46.0	50.0	38.0	47.0	53.0	52.0	52.0	52.0	40.0	35.0	64.0	47.0	36.0	38.0	36.0	18.0	0.0
Catamarca	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Chaco	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	...	3.0	5.0	5.0	2.0	1.0	2.0	1.0	4.0	9.0	7.0	1.0	2.0	5.0	4.0	5.0	4.0	12.0	1.0	3.0	0.0	10.0	2.0	5.0	4.0	3.0	2.0	8.0	5.0	4.0	7.0	5.0	6.0	5.0	1.0	7.0	6.0	4.0	1.0	3.0	0.0
Chubut	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.0	0.0	1.0	3.0	0.0	5.0	7.0	6.0	53.0	2.0	0.0	0.0	11.0	0.0	5.0	3.0	6.0	2.0	0.0	15.0	4.0	0.0	9.0	2.0	0.0	24.0	1.0	7.0	2.0	4.0	8.0	5.0	5.0	2.0	25.0	-1.0	2.0	2.0	2.0	0.0

	Province/State	Country/Region	Lat	Long	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	2/1/20	2/2/20	2/3/20	2/4/20	2/5/20	2/6/20	2/7/20	2/8/20	2/9/20	2/10/20	2/11/20	2/12/20	2/13/20	2/14/20	2/15/20	2/16/20	2/17/20	2/18/20	2/19/20	2/20/20	2/21/20	2/22/20	2/23/20	2/24/20	2/25/20	2/26/20	...	9/22/20	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20
0	NaN	Afghanistan	33.93911	67.709953	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	...	39096	39145	39170	39186	39192	39227	39233	39254	39268	39285	39290	39297	39341	39422	39486	39548	39616	39693	39703	39799	39870	39928	39994	40026	40073	40141	40200	40287	40357	40510	40626	40687	40768	40833	40937	41032	41145	41268	41334	41425
1	NaN	Albania	41.15330	20.168300	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	12666	12787	12921	13045	13153	13259	13391	13518	13649	13806	13965	14117	14266	14410	14568	14730	14899	15066	15231	15399	15570	15752	15955	16212	16501	16774	17055	17350	17651	17948	18250	18556	18858	19157	19445	19729	20040	20315	20634	20875
2	NaN	Algeria	28.03390	1.659600	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	...	50214	50400	50579	50754	50914	51067	51213	51368	51530	51690	51847	51995	52136	52270	52399	52520	52658	52804	52940	53072	53325	53399	53584	53777	53998	54203	54402	54616	54829	55081	55357	55630	55880	56143	56419	56706	57026	57332	57651	57942
3	NaN	Andorra	42.50630	1.521800	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	1681	1753	1753	1836	1836	1836	1966	1966	2050	2050	2110	2110	2110	2370	2370	2568	2568	2696	2696	2696	2995	2995	3190	3190	3377	3377	3377	3623	3623	3811	3811	4038	4038	4038	4325	4410	4517	4567	4665	4756
4	NaN	Angola	-11.20270	17.873900	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	4236	4363	4475	4590	4672	4718	4797	4905	4972	5114	5211	5370	5402	5530	5725	5725	5958	6031	6246	6366	6488	6680	6846	7096	7222	7462	7622	7829	8049	8338	8582	8829	9026	9381	9644	9871	10074	10269	10558	10805
5	NaN	Antigua and Barbuda	17.06080	-61.796400	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	96	97	97	98	98	101	101	101	101	101	106	107	107	107	107	108	111	111	111	111	111	111	112	112	112	119	119	119	119	122	122	122	124	124	124	124	124	124	127	128
6	NaN	Argentina	-38.41610	-63.616700	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	652174	664799	678266	691235	702484	711325	723132	736609	751001	765002	779689	790818	798486	809728	824468	840915	856369	871468	883882	894206	903730	917035	931967	949063	965609	979119	989680	1002662	1018999	1037325	1053650	1069368	1081336	1090589	1102301	1116609	1130533	1143800	1157179	1166924
7	NaN	Armenia	40.06910	45.038200	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	47667	47877	48251	48643	49072	49400	49574	49901	50359	50850	51382	51925	52496	52677	53083	53755	54473	55087	55736	56451	56821	57566	58624	59995	61460	63000	64694	65460	66694	68530	70836	73310	75523	77837	78810	80410	82651	85034	87432	89813
8	Australian Capital Territory	Australia	-35.47350	149.012400	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	113	114	114	114	114	114	114	114	114	114	114	114
9	New South Wales	Australia	-33.86880	151.209300	3	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4	...	4212	4213	4217	4218	4218	4218	4220	4224	4227	4231	4232	4234	4235	4246	4249	4261	4271	4273	4278	4284	4295	4310	4321	4326	4333	4338	4342	4347	4357	4363	4370	4375	4382	4386	4398	4406	4411	4417	4421	4425

index	Province/State	Country/Region	Lat	Long	3/2/20	3/5/20	3/8/20	3/9/20	3/10/20	3/11/20	3/12/20	3/13/20	3/14/20	3/15/20	3/16/20	3/17/20	3/18/20	3/19/20	3/20/20	3/21/20	3/22/20	3/23/20	3/24/20	3/25/20	3/26/20	3/27/20	3/28/20	3/29/20	3/30/20	3/31/20	4/1/20	4/2/20	4/3/20	4/4/20	4/5/20	4/6/20	4/7/20	4/8/20	4/9/20	4/10/20	...	9/22/20	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20
0	Buenos Aires	Argentina	-38.4161	-63.6167	0.0	1.0	9.0	9.0	10.0	11.0	11.0	12.0	12.0	13.0	14.0	15.0	21.0	35.0	44.0	58.0	66.0	71.0	100.0	130.0	157.0	193.0	201.0	217.0	253.0	270.0	280.0	316.0	338.0	364.0	397.0	409.0	443.0	460.0	488.0	507.0	...	377321.0	382710.0	388832.0	394432.0	398912.0	401859.0	406403.0	411731.0	417674.0	423081.0	428776.0	433283.0	435931.0	440402.0	446061.0	451283.0	456467.0	461813.0	465887.0	468429.0	470650.0	475316.0	480491.0	486247.0	491446.0	495865.0	498248.0	502454.0	507435.0	512614.0	517468.0	522117.0	525147.0	527317.0	531011.0	535232.0	539470.0	543178.0	547008.0	549362.0
1	CABA	Argentina	-38.4161	-63.6167	1.0	1.0	1.0	2.0	3.0	4.0	14.0	16.0	27.0	33.0	38.0	42.0	52.0	60.0	69.0	98.0	109.0	120.0	149.0	170.0	200.0	227.0	245.0	258.0	292.0	311.0	321.0	345.0	373.0	407.0	439.0	456.0	480.0	498.0	521.0	532.0	...	118873.0	119802.0	120811.0	121737.0	122654.0	123282.0	124069.0	125062.0	125960.0	126884.0	127934.0	128735.0	129268.0	129952.0	130835.0	131791.0	132728.0	133602.0	134344.0	134986.0	135485.0	136230.0	137055.0	137885.0	138837.0	139447.0	139964.0	140521.0	141184.0	141924.0	142490.0	143075.0	143539.0	143984.0	144499.0	145097.0	145738.0	146250.0	146754.0	147195.0
2	Catamarca	Argentina	-38.4161	-63.6167	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	187.0	193.0	195.0	201.0	205.0	272.0	275.0	279.0	287.0	292.0	299.0	305.0	318.0	318.0	327.0	335.0	353.0	360.0	370.0	376.0	390.0	393.0	401.0	407.0	429.0	453.0	480.0	492.0	500.0	525.0	552.0	563.0	596.0	631.0	638.0	666.0	720.0	736.0	819.0	896.0
3	Chaco	Argentina	-38.4161	-63.6167	0.0	0.0	0.0	5.0	5.0	5.0	5.0	5.0	5.0	9.0	12.0	13.0	14.0	16.0	17.0	22.0	29.0	33.0	42.0	54.0	57.0	60.0	65.0	69.0	81.0	84.0	96.0	99.0	107.0	115.0	115.0	119.0	123.0	138.0	144.0	149.0	...	7573.0	7659.0	7787.0	7907.0	8067.0	8178.0	8243.0	8386.0	8543.0	8743.0	8943.0	9063.0	9240.0	9341.0	9486.0	9693.0	9849.0	10026.0	10174.0	10373.0	10506.0	10678.0	10870.0	11067.0	11264.0	11431.0	11663.0	11848.0	12041.0	12296.0	12550.0	12781.0	12928.0	13090.0	13227.0	13394.0	13580.0	13710.0	13913.0	14050.0
4	Chubut	Argentina	-38.4161	-63.6167	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	2555.0	2758.0	2920.0	3127.0	3309.0	3409.0	3590.0	3799.0	3955.0	4143.0	4357.0	4506.0	4687.0	4921.0	5243.0	5624.0	5936.0	6141.0	6623.0	7002.0	7350.0	7788.0	8024.0	8269.0	8557.0	8892.0	9042.0	9262.0	9727.0	10258.0	10928.0	11601.0	11952.0	12437.0	12981.0	13299.0	13594.0	13878.0	14187.0	14382.0

	Country/Region	Lat	Long	2/24/20	2/25/20	2/26/20	...	9/22/20	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20
0	Afghanistan	33.9391	67.71	1.0	1.0	1.0	...	39096.0	39145.0	39170.0	39186.0	39192.0	39227.0	39233.0	39254.0	39268.0	39285.0	39290.0	39297.0	39341.0	39422.0	39486.0	39548.0	39616.0	39693.0	39703.0	39799.0	39870.0	39928.0	39994.0	40026.0	40073.0	40141.0	40200.0	40287.0	40357.0	40510.0	40626.0	40687.0	40768.0	40833.0	40937.0	41032.0	41145.0	41268.0	41334.0	41425.0
1	Albania	41.1533	20.1683	0.0	0.0	0.0	...	12666.0	12787.0	12921.0	13045.0	13153.0	13259.0	13391.0	13518.0	13649.0	13806.0	13965.0	14117.0	14266.0	14410.0	14568.0	14730.0	14899.0	15066.0	15231.0	15399.0	15570.0	15752.0	15955.0	16212.0	16501.0	16774.0	17055.0	17350.0	17651.0	17948.0	18250.0	18556.0	18858.0	19157.0	19445.0	19729.0	20040.0	20315.0	20634.0	20875.0
2	Algeria	28.0339	1.6596	0.0	1.0	1.0	...	50214.0	50400.0	50579.0	50754.0	50914.0	51067.0	51213.0	51368.0	51530.0	51690.0	51847.0	51995.0	52136.0	52270.0	52399.0	52520.0	52658.0	52804.0	52940.0	53072.0	53325.0	53399.0	53584.0	53777.0	53998.0	54203.0	54402.0	54616.0	54829.0	55081.0	55357.0	55630.0	55880.0	56143.0	56419.0	56706.0	57026.0	57332.0	57651.0	57942.0
3	Andorra	42.5063	1.5218	0.0	0.0	0.0	...	1681.0	1753.0	1753.0	1836.0	1836.0	1836.0	1966.0	1966.0	2050.0	2050.0	2110.0	2110.0	2110.0	2370.0	2370.0	2568.0	2568.0	2696.0	2696.0	2696.0	2995.0	2995.0	3190.0	3190.0	3377.0	3377.0	3377.0	3623.0	3623.0	3811.0	3811.0	4038.0	4038.0	4038.0	4325.0	4410.0	4517.0	4567.0	4665.0	4756.0
4	Angola	-11.2027	17.8739	0.0	0.0	0.0	...	4236.0	4363.0	4475.0	4590.0	4672.0	4718.0	4797.0	4905.0	4972.0	5114.0	5211.0	5370.0	5402.0	5530.0	5725.0	5725.0	5958.0	6031.0	6246.0	6366.0	6488.0	6680.0	6846.0	7096.0	7222.0	7462.0	7622.0	7829.0	8049.0	8338.0	8582.0	8829.0	9026.0	9381.0	9644.0	9871.0	10074.0	10269.0	10558.0	10805.0

	Province/State	Country/Region	Lat	Long	Date	Confirmed
67260	0	Austria	47.5162	14.5501	9/10/20	31247.0
53868	South Australia	Australia	-34.9285	138.601	7/26/20	447.0
39146	0	Georgia	42.3154	43.3569	6/5/20	805.0
16266	0	Thailand	15.87	100.993	3/18/20	212.0
60135	0	Lithuania	55.1694	23.8813	8/16/20	2416.0

	Province/State	Country/Region	Lat	Long	Date	Deaths
36059	0	Turkey	38.9637	35.2433	5/25/20	4369.0
64875	British Virgin Islands	United Kingdom	18.4207	-64.64	9/1/20	1.0
65737	0	Timor-Leste	-8.87422	125.728	9/4/20	0.0
53283	0	Armenia	40.0691	45.0382	7/24/20	692.0
38287	0	India	20.5937	78.9629	6/2/20	5829.0

	Province/State	Country/Region	Lat	Long	Date	Confirmed	Deaths
39257	0	Togo	8.6195	0.8248	2020-06-05	485.0	13.0
25346	Tucumán	Argentina	-38.4161	-63.6167	2020-04-19	30.0	4.0
38769	New Brunswick	Canada	46.5653	-66.4619	2020-06-04	136.0	1.0
7619	0	Brazil	-14.235	-51.9253	2020-02-18	0.0	0.0
37247	0	Zimbabwe	-19.0154	29.1549	2020-05-29	149.0	4.0

	Province/State	Country/Region	Date
63	Diamond Princess	Canada	2020-01-23
64	Grand Princess	Canada	2020-01-23
125	0	Diamond Princess	2020-01-23
191	0	MS Zaandam	2020-01-23
354	Diamond Princess	Canada	2020-01-24

	Date	Province/State	Country/Region	Lat	Long	Confirmed	Deaths	New cases	New deaths
71890	2020-09-29	0	San Marino	43.942400	12.457800	727	42	0	0
67676	2020-09-14	Jujuy	Argentina	-38.416100	-63.616700	12402	298	277	10
15201	2020-03-15	Tibet	China	31.692700	88.092400	1	0	0	0
58347	2020-08-13	0	Korea, South	35.907757	127.766922	14873	305	103	0
27302	2020-04-27	0	Costa Rica	9.748900	-83.753400	697	6	2	0
48417	2020-07-09	Chubut	Argentina	-38.416100	-63.616700	192	1	19	0
24063	2020-04-15	Neuquén	Argentina	-38.416100	-63.616700	91	5	1	0
7369	2020-02-17	Buenos Aires	Argentina	-38.416100	-63.616700	0	0	0	0
2682	2020-02-01	0	Madagascar	-18.766947	46.869107	0	0	0	0
6322	2020-02-14	0	Azerbaijan	40.143100	47.576900	0	0	0	0

	Country/Region	Country Code	Region	IncomeGroup	Population
0	Aruba	ABW	Latin America & Caribbean	High income	106314.0
1	Afghanistan	AFG	South Asia	Low income	38041754.0
2	Angola	AGO	Sub-Saharan Africa	Lower middle income	31825295.0

	Province/State	Country/Region	Lat	Long	...	9/22/20	9/23/20	9/24/20	9/25/20	9/26/20	9/27/20	9/28/20	9/29/20	9/30/20	10/1/20	10/2/20	10/3/20	10/4/20	10/5/20	10/6/20	10/7/20	10/8/20	10/9/20	10/10/20	10/11/20	10/12/20	10/13/20	10/14/20	10/15/20	10/16/20	10/17/20	10/18/20	10/19/20	10/20/20	10/21/20	10/22/20	10/23/20	10/24/20	10/25/20	10/26/20	10/27/20	10/28/20	10/29/20	10/30/20	10/31/20
0	NaN	Afghanistan	33.93911	67.709953	...	1445	1446	1451	1451	1453	1453	1455	1458	1458	1458	1458	1462	1462	1466	1467	1469	1470	1472	1473	1477	1479	1480	1481	1481	1485	1488	1492	1497	1499	1501	1505	1507	1511	1514	1518	1523	1529	1532	1533	1536
1	NaN	Albania	41.15330	20.168300	...	367	370	370	373	375	377	380	384	387	388	389	392	396	400	403	407	411	413	416	420	424	429	434	439	443	448	451	454	458	462	465	469	473	477	480	487	493	499	502	509
2	NaN	Algeria	28.03390	1.659600	...	1689	1698	1703	1707	1711	1714	1719	1726	1736	1741	1749	1756	1760	1768	1768	1771	1783	1789	1795	1801	1809	1818	1827	1827	1841	1846	1856	1865	1873	1880	1888	1897	1907	1914	1922	1931	1941	1949	1956	1964
3	NaN	Andorra	42.50630	1.521800	...	53	53	53	53	53	53	53	53	53	53	53	53	53	53	53	53	54	55	55	55	57	57	59	59	59	59	59	62	62	63	63	69	69	69	72	72	72	73	75	75
4	NaN	Angola	-11.20270	17.873900	...	155	159	162	167	171	174	176	179	183	185	189	193	195	199	211	211	208	212	218	218	219	222	227	228	234	241	247	248	251	255	260	265	267	268	270	271	275	275	279	284

	Values	Importance(%)
Cases	256	0.315189
Deaths	127	0.156364

	index	Lat	Long	Confirmed	Deaths	New cases	New deaths	weekdays	Population	IncomeGroup_code	Confirmed_log	Deaths_log	Infection_rate	Mortality_rate	trend
count	38994.000000	38994.000000	38994.000000	3.899400e+04	38994.000000	38994.000000	38994.000000	38994.000000	3.899400e+04	38994.000000	38994.000000	31639.000000	3.899400e+04	38994.000000	38994.000000
mean	19496.500000	29.396483	34.320005	8.800086e+04	3205.789686	1100.010643	28.352413	6.184541	3.771351e+08	3.900113	10.870535	4.726033	2.807548e+00	0.080112	120.880418
std	11256.742535	120.088950	349.281534	5.015227e+05	15042.657418	5734.100269	141.010994	15.018442	3.865710e+09	12.113998	19.909286	NaN	5.585039e+00	0.176113	70.522227
min	0.000000	-921.986400	-1526.800800	1.000000e+00	0.000000	0.000000	0.000000	1.000000	3.386000e+04	1.000000	0.000000	0.000000	7.318406e-07	0.000000	1.000000
25%	9748.250000	4.860416	-7.692100	3.640000e+02	6.000000	1.000000	0.000000	2.000000	2.786844e+06	1.000000	5.899897	2.890372	6.541807e-02	0.000833	60.000000
50%	19496.500000	18.735700	20.168300	3.105000e+03	60.000000	34.000000	0.000000	4.000000	9.770529e+06	3.000000	8.045909	4.454347	5.289470e-01	0.009605	120.000000
75%	29244.750000	41.871940	43.679291	2.795275e+04	585.000000	319.000000	5.000000	6.000000	3.194978e+07	4.000000	10.365270	6.476972	3.058725e+00	0.060449	180.000000
max	38993.000000	1085.292300	3688.937700	9.125482e+06	230548.000000	124905.000000	4143.000000	231.000000	4.612460e+10	132.000000	224.717285	16.735800	6.165254e+01	1.240402	283.000000

	Province/State	Lat	Long
2642	CABA	-34.614493	-58.445856
4731	Catamarca	-27.335833	-66.947682
6067	San Luis	-33.757726	-66.028130
7251	Catamarca	-27.335833	-66.947682
2193	Formosa	-24.894973	-59.932441

	mean	std	min	max
Confirmed	45967.793085	364324.573237	0.000000e+00	9.125482e+06
Deaths	1674.810941	10968.589688	0.000000e+00	2.305480e+05
New cases	577.020519	4162.367141	0.000000e+00	1.239120e+05
New deaths	14.996038	102.839180	0.000000e+00	4.143000e+03
Confirmed_log	6.835639	NaN	0.000000e+00	1.602658e+01
Deaths_log	3.913282	NaN	0.000000e+00	1.234821e+01
Infection_rate	1.466125	4.244475	0.000000e+00	6.165254e+01
Mortality_rate	0.041845	0.132675	0.000000e+00	1.240402e+00
Confirmed	88000.855208	501522.710227	1.000000e+00	9.125482e+06
Deaths	3205.789686	15042.657418	0.000000e+00	2.305480e+05
New cases	1100.010643	5734.100269	0.000000e+00	1.249050e+05
New deaths	28.352413	141.010994	0.000000e+00	4.143000e+03
Confirmed_log	10.870535	19.909286	0.000000e+00	2.247173e+02
Deaths_log	4.726033	NaN	0.000000e+00	1.673580e+01
Infection_rate	2.807548	5.585039	7.318406e-07	6.165254e+01
Mortality_rate	0.080112	0.176113	0.000000e+00	1.240402e+00

	Lat	Long	Confirmed	Deaths	New cases	New deaths	weekdays	Population	IncomeGroup_code	Confirmed_log	Deaths_log	Infection_rate	Mortality_rate
count	74712.000000	74712.000000	7.471200e+04	74712.000000	74712.000000	74712.000000	74712.000000	7.471200e+04	74712.000000	63837.000000	50427.000000	74712.000000	74712.000000
mean	16.105765	16.294028	4.596779e+04	1674.810941	577.020519	14.996038	4.010601	2.073564e+08	2.556818	6.835639	3.913282	1.466125	0.041845
std	29.131182	73.092687	3.643246e+05	10968.589688	4162.367141	102.839180	1.993792	4.590462e+08	1.321664	NaN	NaN	4.244475	0.132675
min	-82.521518	-135.000000	0.000000e+00	0.000000	0.000000	0.000000	1.000000	3.386000e+04	1.000000	0.000000	0.000000	0.000000	0.000000
25%	-1.060575	-59.528500	1.900000e+01	0.000000	0.000000	0.000000	2.000000	5.657755e+06	1.000000	4.812184	1.791759	0.000195	0.000000
50%	18.578200	17.776500	4.790000e+02	6.000000	1.000000	0.000000	4.000000	2.536431e+07	3.000000	6.831954	3.610918	0.019077	0.000209
75%	40.087600	68.118740	5.065250e+03	94.000000	67.000000	1.000000	6.000000	6.683440e+07	4.000000	9.020873	5.736572	0.654054	0.013066
max	71.706900	178.065000	9.125482e+06	230548.000000	123912.000000	4143.000000	7.000000	1.397715e+09	4.000000	16.026581	12.348214	61.652537	1.240402

	Confirmed	Deaths	New cases	New deaths	Infection_rate	Mortality_rate	Confirmed	Deaths	New cases	New deaths	Infection_rate	Mortality_rate
mean	9112.974382	215.311837	171.852915	4.564340	0.202787	0.004791	4.983833e+05	11714.454545	9068.355372	244.743802	11.090289	0.260676
std	44619.847312	1235.754196	645.087165	43.116718	0.992904	0.027499	3.316445e+05	9234.954975	4004.931743	314.039024	7.379929	0.205501
max	549362.000000	17605.000000	7486.000000	3199.000000	12.224694	0.391756	1.166924e+06	30997.000000	18326.000000	3351.000000	25.967010	0.689762